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author | Georgios Pinitas <georgios.pinitas@arm.com> | 2020-11-21 03:04:18 +0000 |
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committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2020-12-01 10:41:54 +0000 |
commit | 40f51a63c8e7258db15269427ae4fe1ad199c550 (patch) | |
tree | 353253a41863966995a45556731e7181a643c003 /examples/neon_cnn.cpp | |
parent | 327800401c4185d98fcc01b9c9efbc038a4228ed (diff) | |
download | ComputeLibrary-40f51a63c8e7258db15269427ae4fe1ad199c550.tar.gz |
Update default C++ standard to C++14
(3RDPARTY_UPDATE)
Resolves: COMPMID-3849
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I6369f112337310140e2d6c8e79630cd11138dfa0
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4544
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'examples/neon_cnn.cpp')
-rw-r--r-- | examples/neon_cnn.cpp | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp index 85f8792b9c..339c8c1a81 100644 --- a/examples/neon_cnn.cpp +++ b/examples/neon_cnn.cpp @@ -53,10 +53,10 @@ public: // The weights and biases tensors should be initialized with the values inferred with the training // Set memory manager where allowed to manage internal memory requirements - conv0 = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers); - conv1 = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers); - fc0 = arm_compute::support::cpp14::make_unique<NEFullyConnectedLayer>(mm_layers); - softmax = arm_compute::support::cpp14::make_unique<NESoftmaxLayer>(mm_layers); + conv0 = std::make_unique<NEConvolutionLayer>(mm_layers); + conv1 = std::make_unique<NEConvolutionLayer>(mm_layers); + fc0 = std::make_unique<NEFullyConnectedLayer>(mm_layers); + softmax = std::make_unique<NESoftmaxLayer>(mm_layers); /* [Initialize tensors] */ @@ -170,8 +170,8 @@ public: // We need 2 memory groups for handling the input and output // We call explicitly allocate after manage() in order to avoid overlapping lifetimes - memory_group0 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions); - memory_group1 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions); + memory_group0 = std::make_unique<MemoryGroup>(mm_transitions); + memory_group1 = std::make_unique<MemoryGroup>(mm_transitions); memory_group0->manage(&out_conv0); out_conv0.allocator()->allocate(); |